Vehicle Detection - Gaspard Shen

1. HOG

Read the Vehicles and non-Vehicles training images set

In [1]:
import numpy as np
import cv2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import glob
#from skimage.feature import hog
#from skimage import color, exposure
# images are divided up into vehicles and non-vehicles

cars = []
images = glob.glob('vehicles/**/*.png')
for image in images:
    cars.append(image)

notcars = []
images = glob.glob('non-vehicles/**/*.png')
for image in images:
    notcars.append(image)

# Define a function to return some characteristics of the dataset 
def data_look(car_list, notcar_list):
    data_dict = {}
    # Define a key in data_dict "n_cars" and store the number of car images
    data_dict["n_cars"] = len(car_list)
    # Define a key "n_notcars" and store the number of notcar images
    data_dict["n_notcars"] = len(notcar_list)
    # Read in a test image, either car or notcar
    example_img = mpimg.imread(car_list[0])
    # Define a key "image_shape" and store the test image shape 3-tuple
    data_dict["image_shape"] = example_img.shape
    # Define a key "data_type" and store the data type of the test image.
    data_dict["data_type"] = example_img.dtype
    # Return data_dict
    return data_dict
    
data_info = data_look(cars, notcars)

print('Your function returned a count of', 
      data_info["n_cars"], ' cars and', 
      data_info["n_notcars"], ' non-cars')
print('of size: ',data_info["image_shape"], ' and data type:', 
      data_info["data_type"])

for i in range(6):
    # Just for fun choose random car / not-car indices and plot example images   
    car_ind = np.random.randint(0, len(cars))
    notcar_ind = np.random.randint(0, len(notcars))

    # Read in car / not-car images
    #print(cars[car_ind], notcars[notcar_ind])
    car_image = mpimg.imread(cars[car_ind])
    notcar_image = mpimg.imread(notcars[notcar_ind])

    # Plot the examples
    fig = plt.figure(figsize=(10, 12))
    plt.subplot(121)
    plt.imshow(car_image)
    plt.title(cars[car_ind])
    plt.subplot(122)
    plt.imshow(notcar_image)
    plt.title(notcars[notcar_ind])
    plt.show()
Your function returned a count of 8792  cars and 8968  non-cars
of size:  (64, 64, 3)  and data type: float32

Tuning the HOG Parameters

In [2]:
from skimage.feature import hog

# Define a function to return HOG features and visualization
def get_hog_features(img, orient, pix_per_cell, cell_per_block, vis=False, feature_vec=True):
    if vis == True:
        # Use skimage.hog() to get both features and a visualization
        features, hog_image = hog(img, orientations=orient,
                          pixels_per_cell=(pix_per_cell, pix_per_cell), 
                          cells_per_block=(cell_per_block, cell_per_block), 
                          transform_sqrt=False, visualise=True, feature_vector=feature_vec)
        return features, hog_image
    else:      
        # Use skimage.hog() to get features only
        features= hog(img, orientations=orient,
                          pixels_per_cell=(pix_per_cell, pix_per_cell), 
                          cells_per_block=(cell_per_block, cell_per_block), 
                          transform_sqrt=False, visualise=False, feature_vector=feature_vec)
        return features

def convert_color(img, conv='RGB2YCrCb'):
    if conv == 'RGB2GRAY':
        return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
    if conv == 'RGB2YCrCb':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    if conv == 'RGB2LUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
    if conv == 'RGB2YUV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
    if conv == 'RGB2HSV':
        return cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
    if conv == 'RGB2HLS':
        return cv2.cvtColor(img, cv2.COLOR_RGB2HLS)

def ShowCarNonCarHOG(img_car, img_notcar, colorspace='RGB2GRAY', orient=9, pix_per_cell=8, cell_per_block=2):    

    color_carimg = convert_color(img_car, colorspace)
    color_carnotimg = convert_color(img_notcar, colorspace)
    # Call our function with vis=True to see an image output
    features, hog_carimg = get_hog_features(color_carimg, orient, 
                            pix_per_cell, cell_per_block, 
                            vis=True, feature_vec=False)
    features, hog_notcarimg = get_hog_features(color_carnotimg, orient, 
                            pix_per_cell, cell_per_block, 
                            vis=True, feature_vec=False)
    print('Using: ', colorspace, orient, 'orientation ', pix_per_cell, 'pix_per_cell ', cell_per_block, 'cell_per_bap')
    # Plot the examples
    fig = plt.figure(figsize=(18, 5))
    plt.subplot(141)
    plt.imshow(color_carimg, cmap='gray')
    plt.title('Car Image')
    plt.subplot(142)
    plt.imshow(hog_carimg, cmap='gray')
    plt.title('HOG Visualization')
    plt.subplot(143)
    plt.imshow(color_carnotimg, cmap='gray')
    plt.title('Non Car Image')
    plt.subplot(144)
    plt.imshow(hog_notcarimg, cmap='gray')
    plt.title('HOG Visualization')
    plt.show()

# Generate a random index to look at a car image
ind = np.random.randint(0, len(cars))
car_img = mpimg.imread(cars[ind])

ind = np.random.randint(0, len(notcars))
notcar_img = mpimg.imread(notcars[ind])
# Define HOG parameters
orient = 9
pix_per_cell = 8
cell_per_block = 2
colorspace = 'RGB2GRAY'
ShowCarNonCarHOG(car_img, notcar_img, colorspace, orient, pix_per_cell, cell_per_block)

orient = 12
pix_per_cell = 8
cell_per_block = 2
colorspace = 'RGB2GRAY'
ShowCarNonCarHOG(car_img, notcar_img, colorspace, orient, pix_per_cell, cell_per_block)

# Play the pix_per_cell param
orient = 10
pix_per_cell = 16
cell_per_block = 2
colorspace = 'RGB2GRAY'
ShowCarNonCarHOG(car_img, notcar_img, colorspace, orient, pix_per_cell, cell_per_block)

orient = 11
pix_per_cell = 4
cell_per_block = 2
colorspace = 'RGB2GRAY'
ShowCarNonCarHOG(car_img, notcar_img, colorspace, orient, pix_per_cell, cell_per_block)
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)
Using:  RGB2GRAY 9 orientation  8 pix_per_cell  2 cell_per_bap
Using:  RGB2GRAY 12 orientation  8 pix_per_cell  2 cell_per_bap
Using:  RGB2GRAY 10 orientation  16 pix_per_cell  2 cell_per_bap
Using:  RGB2GRAY 11 orientation  4 pix_per_cell  2 cell_per_bap

Combine and Normalize Features

In [3]:
import time
from sklearn.preprocessing import StandardScaler

# Define a function to compute binned color features  
def bin_spatial(img, size=(32, 32)):
    # Use cv2.resize().ravel() to create the feature vector
    features = cv2.resize(img, size).ravel() 
    # Return the feature vector
    return features

# Define a function to compute color histogram features  
def color_hist(img, nbins=32, bins_range=(0, 256)):
    # Compute the histogram of the color channels separately
    channel1_hist = np.histogram(img[:,:,0], bins=nbins, range=bins_range)
    channel2_hist = np.histogram(img[:,:,1], bins=nbins, range=bins_range)
    channel3_hist = np.histogram(img[:,:,2], bins=nbins, range=bins_range)
    # Concatenate the histograms into a single feature vector
    hist_features = np.concatenate((channel1_hist[0], channel2_hist[0], channel3_hist[0]))
    # Return the individual histograms, bin_centers and feature vector
    return hist_features

# Define a function to extract features from a list of images
# Have this function call bin_spatial() and color_hist()
def extract_features(imgs, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True):
    # Create a list to append feature vectors to
    features = []
    # Iterate through the list of images
    for file in imgs:
        file_features = []
        # Read in each one by one
        image = mpimg.imread(file)
        # apply color conversion if other than 'RGB'
        if color_space != 'RGB':
            if color_space == 'HSV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
            elif color_space == 'LUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2LUV)
            elif color_space == 'HLS':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)
            elif color_space == 'YUV':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YUV)
            elif color_space == 'YCrCb':
                feature_image = cv2.cvtColor(image, cv2.COLOR_RGB2YCrCb)
        else: feature_image = np.copy(image)      

        if spatial_feat == True:
            spatial_features = bin_spatial(feature_image, size=spatial_size)
            file_features.append(spatial_features)
        if hist_feat == True:
            # Apply color_hist()
            hist_features = color_hist(feature_image, nbins=hist_bins)
            file_features.append(hist_features)
        if hog_feat == True:
        # Call get_hog_features() with vis=False, feature_vec=True
            if hog_channel == 'ALL':
                hog_features = []
                for channel in range(feature_image.shape[2]):
                    hog_features.append(get_hog_features(feature_image[:,:,channel], 
                                        orient, pix_per_cell, cell_per_block, 
                                        vis=False, feature_vec=True))
                hog_features = np.ravel(hog_features)        
            else:
                hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                            pix_per_cell, cell_per_block, vis=False, feature_vec=True)
            # Append the new feature vector to the features list
            file_features.append(hog_features)
        features.append(np.concatenate(file_features))
    # Return list of feature vectors
    return features

### TODO: Tweak these parameters and see how the results change.
colorspace = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9
pix_per_cell = 8
cell_per_block = 2
hog_channel = 'ALL' # Can be 0, 1, 2, or "ALL"

t=time.time()
car_features = extract_features(cars, color_space=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel)
notcar_features = extract_features(notcars, color_space=colorspace, orient=orient, 
                        pix_per_cell=pix_per_cell, cell_per_block=cell_per_block, 
                        hog_channel=hog_channel)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract HOG features...')

if len(car_features) > 0:
    # Create an array stack of feature vectors
    X = np.vstack((car_features, notcar_features)).astype(np.float64)                        
    # Fit a per-column scaler
    X_scaler = StandardScaler().fit(X)
    # Apply the scaler to X
    scaled_X = X_scaler.transform(X)
    car_ind = np.random.randint(0, len(cars))
    # Plot an example of raw and scaled features
    fig = plt.figure(figsize=(12,4))
    plt.subplot(131)
    plt.imshow(mpimg.imread(cars[car_ind]))
    plt.title('Original Image')
    plt.subplot(132)
    plt.plot(X[car_ind])
    plt.title('Raw Features')
    plt.subplot(133)
    plt.plot(scaled_X[car_ind])
    plt.title('Normalized Features')
    fig.tight_layout()
    plt.show()
else: 
    print('Your function only returns empty feature vectors...')
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)
146.37 Seconds to extract HOG features...

2. Train Classifier

In [4]:
from sklearn.svm import LinearSVC
from sklearn import grid_search
# NOTE: the next import is only valid for scikit-learn version <= 0.17
# for scikit-learn >= 0.18 use:
# from sklearn.model_selection import train_test_split
from sklearn.cross_validation import train_test_split

# Create an array stack of feature vectors
X = np.vstack((car_features, notcar_features)).astype(np.float64)                        
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)

# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))

# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    scaled_X, y, test_size=0.2, random_state=rand_state)

print('Using:',orient,'orientations',pix_per_cell,
    'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))

# Use a linear SVC
svc = LinearSVC()
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')

# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))

# Check the prediction time for a single sample
t=time.time()
n_predict = 10
print('My SVC predicts: ', svc.predict(X_test[0:n_predict]))
print('For these',n_predict, 'labels: ', y_test[0:n_predict])
t2 = time.time()
print(round(t2-t, 5), 'Seconds to predict', n_predict,'labels with SVC')
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.
  "This module will be removed in 0.20.", DeprecationWarning)
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/sklearn/grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.
  DeprecationWarning)
Using: 9 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 8460
18.36 Seconds to train SVC...
Test Accuracy of SVC =  0.9876
My SVC predicts:  [ 0.  1.  1.  0.  1.  0.  0.  0.  0.  0.]
For these 10 labels:  [ 0.  1.  1.  0.  1.  0.  0.  0.  0.  0.]
0.01389 Seconds to predict 10 labels with SVC

3. Sliding Window Implement

Implement the draw_boxes() and slide_window()

In [5]:
# Here is your draw_boxes function from the previous exercise
def draw_boxes(img, bboxes, color=(0, 0, 255), thick=6):
    # Make a copy of the image
    imcopy = np.copy(img)
    # Iterate through the bounding boxes
    for bbox in bboxes:
        # Draw a rectangle given bbox coordinates
        cv2.rectangle(imcopy, bbox[0], bbox[1], color, thick)
    # Return the image copy with boxes drawn
    return imcopy

# Define a function that takes an image,
# start and stop positions in both x and y, 
# window size (x and y dimensions),  
# and overlap fraction (for both x and y)
def slide_window(img, x_start_stop=[None, None], y_start_stop=[None, None], 
                    xy_window=(64, 64), xy_overlap=(0.5, 0.5)):
    # If x and/or y start/stop positions not defined, set to image size
    if x_start_stop[0] == None:
        x_start_stop[0] = 0
    if x_start_stop[1] == None:
        x_start_stop[1] = img.shape[1]
    if y_start_stop[0] == None:
        y_start_stop[0] = 0
    if y_start_stop[1] == None:
        y_start_stop[1] = img.shape[0]
    # Compute the span of the region to be searched    
    xspan = x_start_stop[1] - x_start_stop[0]
    yspan = y_start_stop[1] - y_start_stop[0]
    # Compute the number of pixels per step in x/y
    nx_pix_per_step = np.int(xy_window[0]*(1 - xy_overlap[0]))
    ny_pix_per_step = np.int(xy_window[1]*(1 - xy_overlap[1]))
    # Compute the number of windows in x/y
    nx_buffer = np.int(xy_window[0]*(xy_overlap[0]))
    ny_buffer = np.int(xy_window[1]*(xy_overlap[1]))
    nx_windows = np.int((xspan-nx_buffer)/nx_pix_per_step) 
    ny_windows = np.int((yspan-ny_buffer)/ny_pix_per_step) 
    # Initialize a list to append window positions to
    window_list = []
    # Loop through finding x and y window positions
    # Note: you could vectorize this step, but in practice
    # you'll be considering windows one by one with your
    # classifier, so looping makes sense
    for ys in range(ny_windows):
        for xs in range(nx_windows):
            # Calculate window position
            startx = xs*nx_pix_per_step + x_start_stop[0]
            endx = startx + xy_window[0]
            starty = ys*ny_pix_per_step + y_start_stop[0]
            endy = starty + xy_window[1]
            # Append window position to list
            window_list.append(((startx, starty), (endx, endy)))
    # Return the list of windows
    return window_list

image = mpimg.imread('cutouts/bbox-example-image.jpg')
windows = slide_window(image, x_start_stop=[None, None], y_start_stop=[None, None], 
                    xy_window=(128, 128), xy_overlap=(0.5, 0.5))
                       
window_img = draw_boxes(image, windows, color=(0, 0, 255), thick=6)                    
plt.imshow(window_img)
plt.show()

Implement the single_img_features

In [6]:
# Define a function to extract features from a single image window
# This function is very similar to extract_features()
# just for a single image rather than list of images
def single_img_features(img, color_space='RGB', spatial_size=(32, 32),
                        hist_bins=32, orient=9, 
                        pix_per_cell=8, cell_per_block=2, hog_channel=0,
                        spatial_feat=True, hist_feat=True, hog_feat=True):    
    #1) Define an empty list to receive features
    img_features = []
    #2) Apply color conversion if other than 'RGB'
    if color_space != 'RGB':
        if color_space == 'HSV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
        elif color_space == 'LUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2LUV)
        elif color_space == 'HLS':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)
        elif color_space == 'YUV':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YUV)
        elif color_space == 'YCrCb':
            feature_image = cv2.cvtColor(img, cv2.COLOR_RGB2YCrCb)
    else: feature_image = np.copy(img)      
    #3) Compute spatial features if flag is set
    if spatial_feat == True:
        spatial_features = bin_spatial(feature_image, size=spatial_size)
        #4) Append features to list
        img_features.append(spatial_features)
    #5) Compute histogram features if flag is set
    if hist_feat == True:
        hist_features = color_hist(feature_image, nbins=hist_bins)
        #6) Append features to list
        img_features.append(hist_features)
    #7) Compute HOG features if flag is set
    if hog_feat == True:
        if hog_channel == 'ALL':
            hog_features = []
            for channel in range(feature_image.shape[2]):
                hog_features.extend(get_hog_features(feature_image[:,:,channel], 
                                    orient, pix_per_cell, cell_per_block, 
                                    vis=False, feature_vec=True))      
        else:
            hog_features = get_hog_features(feature_image[:,:,hog_channel], orient, 
                        pix_per_cell, cell_per_block, vis=False, feature_vec=True)
        #8) Append features to list
        img_features.append(hog_features)

    #9) Return concatenated array of features
    return np.concatenate(img_features)

# Define a function you will pass an image 
# and the list of windows to be searched (output of slide_windows())
def search_windows(img, windows, clf, scaler, color_space='RGB', 
                    spatial_size=(32, 32), hist_bins=32, 
                    hist_range=(0, 256), orient=9, 
                    pix_per_cell=8, cell_per_block=2, 
                    hog_channel=0, spatial_feat=True, 
                    hist_feat=True, hog_feat=True):

    #1) Create an empty list to receive positive detection windows
    on_windows = []
    #2) Iterate over all windows in the list
    for window in windows:
        #3) Extract the test window from original image
        test_img = cv2.resize(img[window[0][1]:window[1][1], window[0][0]:window[1][0]], (64, 64))      
        #4) Extract features for that window using single_img_features()
        features = single_img_features(test_img, color_space=color_space, 
                            spatial_size=spatial_size, hist_bins=hist_bins, 
                            orient=orient, pix_per_cell=pix_per_cell, 
                            cell_per_block=cell_per_block, 
                            hog_channel=hog_channel, spatial_feat=spatial_feat, 
                            hist_feat=hist_feat, hog_feat=hog_feat)
        #5) Scale extracted features to be fed to classifier
        test_features = scaler.transform(np.array(features).reshape(1, -1))
        #6) Predict using your classifier
        prediction = clf.predict(test_features)
        #7) If positive (prediction == 1) then save the window
        if prediction == 1:
            on_windows.append(window)
    #8) Return windows for positive detections
    return on_windows

### TODO: Tweak these parameters and see how the results change.
color_space = 'YUV' # Can be RGB, HSV, LUV, HLS, YUV, YCrCb
orient = 9  # HOG orientations
pix_per_cell = 8 # HOG pixels per cell
cell_per_block = 2 # HOG cells per block
hog_channel = "ALL" # Can be 0, 1, 2, or "ALL"
spatial_size = (32, 32) # Spatial binning dimensions
hist_bins = 32    # Number of histogram bins
spatial_feat = True # Spatial features on or off
hist_feat = True # Histogram features on or off
hog_feat = True # HOG features on or off
y_start_stop = [450, 720] # Min and max in y to search in slide_window()
t=time.time()
car_features = extract_features(cars, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)
notcar_features = extract_features(notcars, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to extract feature...')
#print('Num of car feature: ', len(car_features),', Num of not car feature: ', len(notcar_features))
X = np.vstack((car_features, notcar_features)).astype(np.float64)
# Fit a per-column scaler
X_scaler = StandardScaler().fit(X)
# Apply the scaler to X
scaled_X = X_scaler.transform(X)
# Define the labels vector
y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))

# Split up data into randomized training and test sets
rand_state = np.random.randint(0, 100)
X_train, X_test, y_train, y_test = train_test_split(
    scaled_X, y, test_size=0.2, random_state=rand_state)

print('Using:','Color Space: ', color_space, ',', orient,'orientations',pix_per_cell,
    'pixels per cell and', cell_per_block,'cells per block')
print('Feature vector length:', len(X_train[0]))

# Use a linear SVC 
svc = LinearSVC()
# Check the training time for the SVC
t=time.time()
svc.fit(X_train, y_train)
t2 = time.time()
print(round(t2-t, 2), 'Seconds to train SVC...')
# Check the score of the SVC
print('Test Accuracy of SVC = ', round(svc.score(X_test, y_test), 4))
# Check the prediction time for a single sample
t=time.time()
image = mpimg.imread('cutouts/bbox-example-image.jpg')
draw_image = np.copy(image)

# Uncomment the following line if you extracted training
# data from .png images (scaled 0 to 1 by mpimg) and the
# image you are searching is a .jpg (scaled 0 to 255)
image = image.astype(np.float32)/255

windows = slide_window(image, x_start_stop=[None, None], y_start_stop=y_start_stop, 
                    xy_window=(96, 96), xy_overlap=(0.5, 0.5))

hot_windows = search_windows(image, windows, svc, X_scaler, color_space=color_space, 
                        spatial_size=spatial_size, hist_bins=hist_bins, 
                        orient=orient, pix_per_cell=pix_per_cell, 
                        cell_per_block=cell_per_block, 
                        hog_channel=hog_channel, spatial_feat=spatial_feat, 
                        hist_feat=hist_feat, hog_feat=hog_feat)                       
t2=time.time()
print(round(t2-t, 2), 'Seconds to Search Hot Windows...')
window_img = draw_boxes(draw_image, hot_windows, color=(0, 0, 255), thick=6)                    

plt.imshow(window_img)
plt.show()
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)
156.1 Seconds to extract feature...
Using: Color Space:  YUV , 9 orientations 8 pixels per cell and 2 cells per block
Feature vector length: 8460
19.25 Seconds to train SVC...
Test Accuracy of SVC =  0.991
1.66 Seconds to Search Hot Windows...
In [9]:
# Define a single function that can extract features using hog sub-sampling and make predictions
def find_carsbbox(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins):
    
    draw_img = np.copy(img)
    img = img.astype(np.float32)/255
    
    img_tosearch = img[ystart:ystop,:,:]
    ctrans_tosearch = convert_color(img_tosearch, conv='RGB2YUV')
    if scale != 1:
        imshape = ctrans_tosearch.shape
        ctrans_tosearch = cv2.resize(ctrans_tosearch, (np.int(imshape[1]/scale), np.int(imshape[0]/scale)))
        
    ch1 = ctrans_tosearch[:,:,0]
    ch2 = ctrans_tosearch[:,:,1]
    ch3 = ctrans_tosearch[:,:,2]

    # Define blocks and steps as above
    nxblocks = (ch1.shape[1] // pix_per_cell) - cell_per_block + 1
    nyblocks = (ch1.shape[0] // pix_per_cell) - cell_per_block + 1 
    nfeat_per_block = orient*cell_per_block**2
    
    # 64 was the orginal sampling rate, with 8 cells and 8 pix per cell
    window = 64
    nblocks_per_window = (window // pix_per_cell) - cell_per_block + 1
    cells_per_step = 2  # Instead of overlap, define how many cells to step
    nxsteps = (nxblocks - nblocks_per_window) // cells_per_step
    nysteps = (nyblocks - nblocks_per_window) // cells_per_step
    
    # Compute individual channel HOG features for the entire image
    hog1 = get_hog_features(ch1, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog2 = get_hog_features(ch2, orient, pix_per_cell, cell_per_block, feature_vec=False)
    hog3 = get_hog_features(ch3, orient, pix_per_cell, cell_per_block, feature_vec=False)
    
    window_list = []
    for xb in range(nxsteps):
        for yb in range(nysteps):
            ypos = yb*cells_per_step
            xpos = xb*cells_per_step
            # Extract HOG for this patch
            hog_feat1 = hog1[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_feat2 = hog2[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_feat3 = hog3[ypos:ypos+nblocks_per_window, xpos:xpos+nblocks_per_window].ravel() 
            hog_features = np.hstack((hog_feat1, hog_feat2, hog_feat3))

            xleft = xpos*pix_per_cell
            ytop = ypos*pix_per_cell

            # Extract the image patch
            subimg = cv2.resize(ctrans_tosearch[ytop:ytop+window, xleft:xleft+window], (64,64))
          
            # Get color features
            spatial_features = bin_spatial(subimg, size=spatial_size)
            hist_features = color_hist(subimg, nbins=hist_bins)
            # Scale features and make a prediction
            test_features = X_scaler.transform(np.hstack((spatial_features, hist_features, hog_features)).reshape(1, -1))
            test_prediction = svc.predict(test_features)
            
            if test_prediction == 1:
                xbox_left = np.int(xleft*scale)
                ytop_draw = np.int(ytop*scale)
                win_draw = np.int(window*scale)
                window_list.append(((xbox_left, ytop_draw+ystart), (xbox_left+win_draw, ytop_draw+win_draw+ystart)))
                cv2.rectangle(draw_img,(xbox_left, ytop_draw+ystart),(xbox_left+win_draw,ytop_draw+win_draw+ystart),(0,0,255),6) 
                
    return draw_img, window_list

ystart = 360
ystop = 700
scale = 1.5

img = mpimg.imread('test_images/test1.jpg')
bb_img, box_list = find_carsbbox(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)

plt.imshow(bb_img)
plt.show()
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)

4. Multiple Detection & False Positives

In [12]:
from scipy.ndimage.measurements import label

# heatmap
heat = np.zeros_like(img[:,:,0]).astype(np.float)

def add_heat(heatmap, bbox_list):
    # Iterate through list of bboxes
    for box in bbox_list:
        # Add += 1 for all pixels inside each bbox
        # Assuming each "box" takes the form ((x1, y1), (x2, y2))
        heatmap[box[0][1]:box[1][1], box[0][0]:box[1][0]] += 1

    # Return updated heatmap
    return heatmap# Iterate through list of bboxes
    
def apply_threshold(heatmap, threshold):
    # Zero out pixels below the threshold
    heatmap[heatmap <= threshold] = 0
    # Return thresholded map
    return heatmap

def draw_labeled_bboxes(img, labels):
    # Iterate through all detected cars
    for car_number in range(1, labels[1]+1):
        # Find pixels with each car_number label value
        nonzero = (labels[0] == car_number).nonzero()
        # Identify x and y values of those pixels
        nonzeroy = np.array(nonzero[0])
        nonzerox = np.array(nonzero[1])
        # Define a bounding box based on min/max x and y
        bbox = ((np.min(nonzerox), np.min(nonzeroy)), (np.max(nonzerox), np.max(nonzeroy)))
        # Draw the box on the image
        cv2.rectangle(img, bbox[0], bbox[1], (0,255,0), 6)
    # Return the image
    return img

# Add heat to each box in box list
heat = add_heat(heat,box_list)
    
# Apply threshold to help remove false positives
heat = apply_threshold(heat,1)

# Visualize the heatmap when displaying    
heatmap = np.clip(heat, 0, 255)

# Find final boxes from heatmap using label function
labels = label(heatmap)
draw_img = draw_labeled_bboxes(np.copy(img), labels)

fig = plt.figure(figsize=(10,5))
plt.subplot(121)
plt.imshow(draw_img)
plt.title('Car Positions')
plt.subplot(122)
plt.imshow(heatmap, cmap='hot')
plt.title('Heat Map')
fig.tight_layout()
plt.show()

Apply to all the test images

In [11]:
images = glob.glob('test_images/test*.jpg')
images = sorted(images)
f, ax = plt.subplots(len(images), 3, figsize=(20, len(images)*5))

heatmap_threshold = 1
i = 0
for fname in images:
    img = mpimg.imread(fname)
    #bboxes
    bb_img, box_list = find_carsbbox(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    ax[i][0].imshow(bb_img)
    ax[i][0].set_title(fname.split('/')[-1], fontsize=20)
    
    # Heatmap
    heat = np.zeros_like(img[:,:,0]).astype(np.float)
    heat = add_heat(heat, box_list)
    heat = apply_threshold(heat, heatmap_threshold)
    heatmap = np.clip(heat, 0, 255)
    ax[i][1].imshow(heatmap, cmap='hot')
    ax[i][1].set_title('Heatmap', fontsize=20)
    
    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    draw_img = draw_labeled_bboxes(np.copy(img), labels)
    ax[i][2].imshow(draw_img)
    ax[i][2].set_title('Car Position', fontsize=20)

    i = i +1
plt.show()  
/Users/gaspard/miniconda3/envs/carnd-term1/lib/python3.5/site-packages/skimage/feature/_hog.py:119: skimage_deprecation: Default value of `block_norm`==`L1` is deprecated and will be changed to `L2-Hys` in v0.15
  'be changed to `L2-Hys` in v0.15', skimage_deprecation)

5. Test on Videos

In [13]:
def process_image(img):
    bb_img, box_list = find_carsbbox(img, ystart, ystop, scale, svc, X_scaler, orient, pix_per_cell, cell_per_block, spatial_size, hist_bins)
    
    heat = np.zeros_like(img[:,:,0]).astype(np.float)
    heat = add_heat(heat, box_list)
    heat = apply_threshold(heat, heatmap_threshold)
    heatmap = np.clip(heat, 0, 255)
    
    # Find final boxes from heatmap using label function
    labels = label(heatmap)
    out_img = draw_labeled_bboxes(np.copy(img), labels)
    
    return out_img
In [14]:
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML

project_output = 'project_video_out.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("project_video.mp4")
output_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time output_clip.write_videofile(project_output, audio=False)
[MoviePy] >>>> Building video project_video_out.mp4
[MoviePy] Writing video project_video_out.mp4
100%|█████████▉| 1260/1261 [20:08<00:00,  1.19it/s]
[MoviePy] Done.
[MoviePy] >>>> Video ready: project_video_out.mp4 

CPU times: user 31min 55s, sys: 58.1 s, total: 32min 53s
Wall time: 20min 10s
In [15]:
HTML("""
<video width="960" height="540" controls>
  <source src="{0}">
</video>
""".format(project_output))
Out[15]:
In [ ]: